System and method for pharmacovigilance

ABSTRACT

A method, computer-readable storage medium, and system for analyzing a relationship between one or more agents and one or more clinical outcomes. The method includes: receiving a selection of one or more agents; receiving a selection of one or more clinical outcomes; for each of the one or more agents, analyzing clinical data stored in a database to determine a number of occurrences of each of the one or more clinical outcomes when the agent is administered; for each of the one or more agents, calculating a risk score for each clinical outcome corresponding to the number of occurrences of the clinical outcome; and outputting the risk scores to a graphical display.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 13/733,791, filed on Jan. 3, 2013, which is incorporated byreference herein in its entirety.

FIELD

This disclosure relates generally to the field of health care managementand, more specifically, to a system and method for pharmacovigilance.

BACKGROUND

Pharmacovigilance is the science of collecting, monitoring, researching,assessing, and evaluating information from healthcare providers andpatients on the adverse effects of medications with a view towardsidentifying hazards associated with the medications and preventing harmto patients.

A typical health care system includes a variety of participants,including doctors, hospitals, insurance carriers, and patients, amongothers. These participants frequently rely on each other for theinformation necessary to perform their respective roles becauseindividual care is delivered and paid for in numerous locations byindividuals and organizations that are typically unrelated. As a result,a plethora of health care information storage and retrieval systems arerequired to support the heavy flow of information between theseparticipants related to patient care. Critical patient data is storedacross many different locations using legacy mainframe and client-serversystems that may be incompatible and/or may store information innon-standardized formats. To ensure proper patient diagnosis andtreatment, health care providers often request patient information byphone or fax from hospitals, laboratories, or other providers.Therefore, disparate systems and information delivery proceduresmaintained by a number of independent health care system constituentslead to gaps in timely delivery of critical information and compromisethe overall quality of clinical care. Since a typical health carepractice is concentrated within a given specialty, an average patientmay be using services of a number of different specialists, eachpotentially having only a partial view of the patient's medical status.

Moreover, pharmacovigilance is facing increased pressure from regulatorsand academics who are mining real-world databases for safety signals.Some factors affecting the pharmacovigilance landscape include: anincreasing use of real-world data by regulators; heightened expectationsof manufacturers from the FDA (Food and Drug Administration), public,and academics/investigators; externalization of safety data (e.g., EMR(electronic medical records); and emergence of pharmacovigilance as anapplied science.

There are certain limitations to the way in which pharmacovigilance iscurrently being implemented. Firstly, pharmacovigilance, or drugsurveillance, is typically done by “ad hoc” reporting, where a physicianindependently identifies patients that have a problem with a certaindrug and report this singular instance to the FDA. The FDA thenaccumulates this information and communicates with pharmaceuticalmanufacturers. This process is inefficient and ineffective. To overcomesome of the drawbacks of the ad hoc approach, the FDA has implementedthe “Sentinel” and “Mini Sentinel” initiatives. However, theseinitiatives look at retrospective and/or historical data to perform drugsurveillance.

Accordingly, there remains a need in the art for a system and method forpharmacovigilance that overcomes the drawbacks and limitations ofcurrent approaches.

SUMMARY

Some embodiments of the disclosure provide a method, computer-readablestorage medium, and system for analyzing a relationship between one ormore agents and one or more clinical outcomes. The method includes:receiving a selection of one or more agents; receiving a selection ofone or more clinical outcomes; for each of the one or more agents,analyzing clinical data stored in a database to determine a number ofoccurrences of each of the one or more clinical outcomes when the agentis administered; for each of the one or more agents, calculating a riskscore for each clinical outcome corresponding to the number ofoccurrences of the clinical outcome; and outputting the risk scores to agraphical display.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an overview of a system foranalyzing a relationship between one or more agents and one or moreclinical outcomes, in accordance with an embodiment of the disclosure.

FIG. 2 is a flow diagram illustrating a method for analyzing arelationship between one or more agents and one or more clinicaloutcomes, in accordance with an embodiment of the disclosure.

FIG. 3 is a screenshot of a user interface displaying an averagerelative risk for plurality of agents versus a plurality of outcomes, inaccordance with an embodiment of the disclosure.

FIG. 4 is a screenshot of a user interface displaying an averagerelative risk for different agents in the same class of agents relativeto a particular outcome, in accordance with an embodiment of thedisclosure.

FIG. 5 is a screenshot of a user interface displaying an averagerelative risk for one agent relative to one outcome, where the data issorted by one or more filters, in accordance with an embodiment of thedisclosure.

FIGS. 6-8 are screenshots of user interfaces displaying an averagerelative risk for a plurality of outcomes for one agent relative toother agents in the same class of agents, in accordance with severalembodiments of the disclosure.

FIG. 9 is a screenshot of a user interface displaying an averagerelative risk for two agents relative to a plurality of outcomes, wherethe data is filtered by gender and age, in accordance with an embodimentof the disclosure.

FIG. 10 is a schematic diagram illustrating an overview of a system foranalyzing a relationship between one or more agents and one or moreclinical outcomes, in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION

Embodiments of the disclosure provide a system and method forpharmacovigilance. According to some embodiments, health relatedclinical data is stored in one or more databases. The clinical data mayinclude, for each patient, demographic data, diagnostic codes, procedurecodes, medication and prescription data, and lab data, among others.Clinical data may include also data from electronic medical records(EMRs). A processor is configured to receive a selection of one or moreagents (e.g., drugs) and one or more clinical outcomes (e.g., adverseevents). The processor is configured to calculate a risk score for theone or more clinical outcomes in relation to the one or more agents.According to various embodiments, the risk score may be an absolute riskor a relative risk. A chi-squared statistical analysis and a p-valuestatistical analysis may also be performed to confirm or reject theobserved calculations.

Accordingly, some embodiments provide a proactive, prospective, andongoing approach to pharmacovigilance. The database from which theanalysis is performed is continuously being updated with new clinicaldata. For example, medical claims data may be entered into the databasewithin 48 hours of an insurance carrier receiving information about thetreatment.

Some embodiments disclosed herein provide a proactive and automatedsignal detection and surveillance system with standardized reporting.Some embodiments provide real-time monitoring due to rapid adjudicationand incorporation of claims data into analytic database, and a signalvalidation system that can exonerate or stratify risk in near real-timeand identify potential benefits, versus an industry average of six tonine months.

Turning to the figures, FIG. 1 is a schematic diagram illustrating anoverview of a system for analyzing a relationship between one or moreagents and one or more clinical outcomes, in accordance with anembodiment of the disclosure. A health care organization 100 collectsand processes a wide spectrum of medical care information relating to apatient 102 in order to analyze the relationship between one or moreagents and one or more clinical outcomes. A personal health record (PHR)108 of a patient 102 may be configured to solicit the patient's inputfor entering additional pertinent medical information, trackingfollow-up actions, and allowing the health care organization 100 totrack the patient's medical history.

When the patient 102 utilizes the services of one or more health careproviders 110, a medical insurance carrier 112 collects the associatedclinical data 114 in order to administer the health insurance coveragefor the patient 102. Additionally, a health care provider 110, such as aphysician or nurse, enters clinical data 114 into one or more healthcare provider applications pursuant to a patient-health care providerinteraction during an office visit or a disease management interaction.Clinical data 114 originates from medical services claims, pharmacydata, as well as from lab results, and includes information associatedwith the patient-health care provider interaction, including informationrelated to the patient's diagnosis and treatment, medical procedures,drug prescription information, in-patient information, and health careprovider notes, among other things. The medical insurance carrier 112and the health care provider 110, in turn, provide the clinical data 114to the health care organization 100, via one or more networks 116, forstorage in one or more medical databases 118. The medical databases 118are administered by one or more server-based computers associated withthe health care provider 100 and comprise one or more medical data fileslocated on a computer-readable medium, such as a hard disk drive, aCD-ROM, a tape drive, or the like. The medical databases 118 may includea commercially available database software application capable ofinterfacing with other applications, running on the same or differentserver based computer, via a structured query language (SQL). In anembodiment, the network 116 is a dedicated medical records network.Alternatively, or in addition, the network 116 includes an Internetconnection that comprises all or part of the network.

In some embodiments, an on-staff team of medical professionals withinthe health care organization 100 consults various sources of healthreference information 122, including evidence-based preventive healthdata, to establish and continuously or periodically revise a set ofclinical rules 120 that reflect best evidence-based medical standards ofcare for a plurality of conditions. The clinical rules 120 are stored inthe medical database 118.

To supplement the clinical data 114 received from the insurance carrier112, the PHR 108 allows patient entry of additional pertinent medicalinformation that is likely to be within the realm of patient'sknowledge. Examples of patient-entered data include additional clinicaldata, such as patient's family history, use of non-prescription drugs,known allergies, unreported and/or untreated conditions (e.g., chroniclow back pain, migraines, etc.), as well as results of self-administeredmedical tests (e.g., periodic blood pressure and/or blood sugarreadings). Preferably, the PHR 108 facilitates the patient's task ofcreating a complete health record by automatically populating the datafields corresponding to the information derived from the medical claims,pharmacy data and lab result-based clinical data 114. In one embodiment,patient-entered data also includes non-clinical data, such as upcomingdoctor's appointments. In some embodiments, the PHR 108 gathers at leastsome of the patient-entered data via a health risk assessment tool (HRA)130 that requests information regarding lifestyle, behaviors, familyhistory, known chronic conditions (e.g., chronic back pain, migraines,etc.), and other medical data, to flag individuals at risk for one ormore predetermined medical conditions (e.g., cancer, heart disease,diabetes, risk of stroke, etc.) pursuant to the processing by acalculation engine 126. Preferably, the HRA 130 presents the patient 102with questions that are relevant to his or her medical history andcurrently presented conditions. The risk assessment logic branchesdynamically to relevant and/or critical questions, thereby saving thepatient time and providing targeted results. The data entered by thepatient 102 into the HRA 130 also populates the corresponding datafields within other areas of PHR 108. The health care organization 100aggregates the clinical data 114 and the patient-entered data, as wellas the health reference and medical news information 122, into themedical database 118 for subsequent processing via the calculationengine 126.

The health care organization 100 includes a multi-dimensional analyticalsoftware application including a calculation engine 126 comprisingcomputer-readable instructions for performing statistical analysis onthe contents of the medical databases 118 in order to analyze arelationship between one or more agents and one or more clinicaloutcomes. The relationships identified by the calculation engine 126 canbe presented in a graphical display 104, e.g., to the healthcareprovider 110 and/or medical insurance carrier 112 and/or to thegovernment (e.g., FDA).

After collecting the relevant data, the calculation engine 126 receivesa selection of one or more agents. In one example implementation, theagents are prescription drugs. The calculation engine calculates a riskof occurrence of one or more clinical outcomes for each of the one ormore agents. In one implementation, a drug may be exonerated fromcausing a clinical outcome that is detected, for example, in spontaneousreports or for specific subgroups (or possibly overall). In anotherexample implementation, the calculation engine 126 may determine thatcertain adverse events occur mostly in off-label use. “Off-label” userefers to non-recommended uses of a drug, such as non-FDA approved uses.In another implementation, calculation engine 126 may determine how adrug's safety profile compares to other drugs within the same class ofdrugs. Other use cases are also within the scope of embodiments of thedisclosure, as described in greater detail herein.

For example, embodiments disclosed herein can provide “comparativeeffectiveness” information by directly comparing multiplepharmacologically similar agents against varied and multiple healthoutcomes of interest, allowing for inferences to be made about thecomparative risks and benefits of these agents. This analysis may leadto the identification of new therapeutic indications for existingagents.

While the entity relationships described above are representative, thoseskilled in the art will realize that alternate arrangements arepossible. In one embodiment, for example, the health care organization100 and the medical insurance carrier 112 is the same entity.Alternatively, the health care organization 100 is an independentservice provider engaged in collecting, aggregating, and processingmedical care data from a plurality of sources to provide a personalhealth record (PHR) service for one or more medical insurance carriers112. In yet another embodiment, the health care organization 100provides PHR services to one or more employers by collecting data fromone or more medical insurance carriers 112.

FIG. 2 is a flow diagram illustrating a method 200 for analyzing arelationship between one or more agents and one or more clinicaloutcomes, in accordance with an embodiment of the disclosure. As shown,the method 200 begins at step 202, where a processor, such as aprocessor associated with the calculation engine 126, receives aselection of an agent. In one embodiment, the agent is a prescriptiondrug. At step 204, the processor receives selection of an adverse event.In some embodiments, adverse events are clinic events. Non-limitingexamples include accidents, cancer, congestive heart failure,depression, diarrhea, glaucoma, infection, liver dysfunction, lymphoma,major bleeding, renal failure, seizures, sudden death, suicide, amongmany others. In some embodiments, the adverse events are coded accordingto standard external definitions (for example, by the government). Inother embodiments, the adverse events are coded according to proprietarydefinitions.

At step 206, the processor analyzes clinical data in a database todetermine a number of occurrences of the adverse event when the agent isadministered. As described, the clinical data can come from manysources, including demographic data, claims data, procedure codes,diagnostic codes, pharmacy/prescription data, patient-entered data,among others. The processor analyzes the data to identify a number ofpatients that have exhibited the adverse event when taking the drug fora predetermined minimum amount of time (for example, 6 months).

At step 208, the processor applies one or more filters. The clinicaldata can be filtered according to certain parameters, such as patientage, gender, demographic info, clinical stratification scores andidentified conditions, and whether the use of the drug was “on-label” or“off-label” (i.e., “on-label” refers to use in the recommended or FDAapproved manner; “off-label” refers to use in a non-recommended ornon-FDA approved manner), among others. The analysis performed at step206 can, therefore, be applied only to the data that satisfies thefilters. In some embodiments, step 208 is performed before step 206.Also, in some embodiments, step 208 is optional and is omitted. In sucha case, no filter is applied, and all the clinical data is analyzed.

At step 210, the processor calculates a risk score corresponding to theadverse event and the agent. According to some embodiments, the riskscore can be an absolute risk or a relative risk. Table 1 belowillustrates occurrences of the adverse event when a particular drug isadministered, a total number of patients that suffered the adverseevent, a total number of patients to whom the drug was administered, anda total number of patients to whom the drug was not administered.

TABLE 1 Drug No Drug Total Adverse IAO IO Event No Adverse Event TotalIA I

In Table 1, “IAO” refers to the occurrence of the adverse event when thedrug is administered, “IO” refers to the total number of patients thatsuffered the adverse event, “IA” refers to the total number of patientsto whom the drug was administered, and “I” refers to the total number ofpatients to whom the drug was not administered.

According to one embodiment, an “ON agent risk,” “NO agent risk,”“Absolute Risk,” and “Relative Risk” can be calculated using Equations 1to 4, respectively:

$\begin{matrix}{{{ONagentRisk} = \frac{IAO}{IA}},} & \left( {{Equation}\mspace{14mu} 1} \right) \\{{{NOagentRisk} = \frac{{IO} - {IAO}}{I - {IA}}},} & \left( {{Equation}\mspace{14mu} 2} \right) \\{{{AbsoluteRisk} = {{ONagentRisk} - {NOagentRisk}}},{and}} & \left( {{Equation}\mspace{14mu} 3} \right) \\{{RelativeRisk} = {\frac{ONagentRisk}{NOagentRisk}.}} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

A “chi-squared” analysis can also be performed to calculate a confidencelevel for the statistical analysis performed using Equation 5:

$\begin{matrix}{\chi^{2} = {\frac{{(I)\left\lbrack {{({IAO})\left( {I - {IO} - {IA} + {IAO}} \right)} - {\left( {{IO} - {IAO}} \right)\left( {{IA} - {IAO}} \right)}} \right\rbrack}^{2}}{({IA})\left( {I - {IA}} \right)({IO})\left( {I - {IO}} \right)}.}} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

In some embodiments, a “P-value” may be calculated to test thestatistical significance of the calculations.

Table 2, below, illustrates an example where the adverse event iscongestive heart failure (CHF) and the drug is an ACE inhibitor.

TABLE 2 Drug No Drug Total Adverse 568 2433 Event No Adverse Event Total179499 656938

As shown, a total of 179499 patients took the drug and 568 experiencedthe adverse effect. A total of 2433 patients experienced the adverseeffect. A total of 656938 patients did not take the drug.

Using the Equations 1-4 above, the relative risk is calculated at 0.81.The chi-squared value is calculated using Equation 5 as 19.49.

At step 212, the processor determines whether there are more adverseevents to analyze for the selected agent/drug. If the processordetermines that there are more adverse events to analyze for theselected agent/drug, then the method 200 returns to step 204, describedabove. If the processor determines that there are no more adverse eventsto analyze for the selected agent/drug, then the method 200 proceeds tostep 214.

At step 214, the processor determines whether there are moreagents/drugs to analyze against adverse events. If the processordetermines that there are more agents/drugs to analyze, then the method200 returns to step 202, described above. If the processor determinesthat there are no more agents/drugs to analyze, then the method 200proceeds to step 216.

At step 216, the processor outputs results (i.e., risk scores) to agraphical display. In some embodiments, the results may be graphicallyrepresented as a “heat map,” where a circle corresponds to the averagerelative risk of the drug-adverse event combination, and where a greatersize of the circle corresponds to a greater average relative risk.Examples are provided below in FIGS. 3-9.

FIG. 3 is a screenshot of a user interface displaying an averagerelative risk for plurality of agents versus a plurality of outcomes, inaccordance with an embodiment of the disclosure. As shown, a listing ofdifferent agents (for example, prescription drugs) is shown along avertical axis 304 and a listing of different outcomes (for example,adverse clinical events) is shown along a horizontal axis 306. Aselection of which agents and/or outcomes are shown in the userinterface can be made via interface element 308 via one or morecheckboxes. Note, in FIG. 3, the selection of different outcomes is notshown (i.e., a user would need to “scroll down” to see the checkboxesfor the different outcomes).

As described above, a processor can calculate a risk score, such asaverage relative risk, for each combination of agent and outcome. In theexample shown in FIG. 3, average relative risk is graphically displayedsuch that an increase in the size 302 of the circle shown for theparticular agent-outcome combination corresponds to an increase in theaverage relative risk. For example, a high average relative risk isexhibited between the agent “Clozapine” and the outcome “Hip fracture,”displayed as circle 310.

FIG. 4 is a screenshot of a user interface displaying an averagerelative risk for different agents in the same class of agents relativeto a particular outcome, in accordance with an embodiment of thedisclosure. In the example shown in FIG. 4, three different bloodthinners are shown along a vertical axis 402 relative to a particularoutcome (e.g., major bleeding) along a horizontal axis 404. In theexample shown, the three blood thinners are “Dabigatran,” “Prasugrel,”and “oral antiplatelet agents other than Prasugrel.” With respect theparticular outcome shown, it is readily apparent from the sizes of thecircles, that the agent “oral antiplatelet agents other than Prasugrel”has the lowest average relative risk of the three agents. Providing agraphical representation of the average relative risk provides for asuperior user experience, when compared to conventional techniques.

FIG. 5 is a screenshot of a user interface displaying an averagerelative risk for one agent relative to one outcome, where the data issorted by one or more filters, in accordance with an embodiment of thedisclosure. As described, the data can be filtered using one or morefilters prior to performing the statistical analysis. In the exampleshown in FIG. 5, a single outcome (e.g., major bleeding) is shown alonga horizontal axis 504. Along the vertical axis 502, a single agent isshown (e.g., “Dabigatran”), where the data is first filtered by gender506 and then by indication 508. Filtering by “indication,” in thisexample, refers to whether the drug was used in an FDA approved manner(i.e., “on-label”) or a non-FDA approved manner (i.e., “off-label”). Inthe example in FIG. 5, A.Fib “Non-Valvular” refers to the FDA approvedmode of administering Dabigatran, and A.Fib “Valvular” refers to thenon-FDA approved mode of administering Dabigatran. When comparing theaverage relative risk for the four different combinations of gender 506and indication 508, the outcome has a similar average relative risk forboth indications (i.e., Non-Valvular and Valvular) for females. However,for males, the Valvular (i.e., non-FDA approved) mode of administeringthe drug has a significantly greater average relative risk. The outcomeshown in FIG. 5 may suggest that a blanket statement from the FDA thatprohibits Valvular treatment with Dabigatran (for both males andfemales) is not necessary, and that the FDA should consider allowingValvular treatments for women. The results shown using embodiments ofthe disclosure are not meant to be definitive proof that certain drugsdo not cause certain complications/outcomes, but rather to generate ahypothesis for further investigation and/or research.

In addition, in some embodiments, a user can click on or hover a cursorover one of the circles, which causes a dialog box 510 to be displayed.The dialog box 510 includes various counts and statistics for theparticular agent-outcome pair.

FIGS. 6-8 are screenshots of user interfaces displaying an averagerelative risk for a plurality of outcomes for one agent relative toother agents in the same class of agents, in accordance with severalembodiments of the disclosure.

In FIG. 6, two different outcomes are shown along the horizontal axis604 (i.e., CHF (congestive heart failure) and sudden death). Along thevertical axis 602, a single agent is shown (i.e., “Lisinopril,” an ACEinhibitor) along with an agent grouping (i.e., “ACE-I”), whichcorresponds to all ACE inhibitors, including the single agent shownseparately. As shown in the example in FIG. 6 via circles 606,Lisinopril has a similar average relative risk for CHF as all ACEinhibitors. However, as shown via circles 608, Lisinopril has a higheraverage relative risk for sudden death compared to all ACE inhibitors.This finding could cause physicians and/or the FDA to place certainwarnings on Lisinopril.

In FIG. 7, two different outcomes are shown along the horizontal axis704 (i.e., diarrhea and infections). Along the vertical axis 702, fivedifferent agents from the same class are shown. In this example, fivedifferent proton pump inhibitors are shown. As shown in the example inFIG. 7 via circles 706, each of the five proton pump inhibitors has asimilar average relative risk for diarrhea. However, with respect toinfections, “Prevacid” has a lower average relative risk as compared tothe other four proton pump inhibitors, as evidenced by the smaller sizeof circle 708. As such, in one example, this information tends to showthat Prevacid may be superior to the other proton pump inhibitors sincethe risk for diarrhea is roughly the same as for the other proton pumpinhibitors, but with a lower risk for infections.

In FIG. 8, five different outcomes are shown along the horizontal axis804. Along the vertical axis 802, two different agents from the sameclass are shown. In this example, two different antibiotics are shown,amoxicillin and azithromycin. As shown in the example in FIG. 8 viacircles 806, both antibiotics have similar average relative risk forfour of the five outcomes shown. However, with respect to the outcome“sudden death,” azithromycin has a relatively large average relativerisk (as shown via circle 808) and amoxicillin has a very low (or evencalculated “zero”) average relative risk for sudden death. Furtherinvestigation into this outcome can be performed by applying filters, asshown in FIG. 9.

FIG. 9 is a screenshot of a user interface displaying an averagerelative risk for two agents relative to a plurality of outcomes, wherethe data is filtered by gender and age, in accordance with an embodimentof the disclosure. In FIG. 9, five different outcomes are shown alongthe horizontal axis 904. Along the vertical axis 902, two differentagents from the same class are shown. In this example, two differentantibiotics are shown, amoxicillin and azithromycin. The agents arefiltered first by gender 906 and then by age band 908. For theparticular outcome in question, “Sudden Death” 910, filtering the databy gender and age band reveals that azithromycin has a relatively highaverage relative risk for sudden death for women ages 45-56. In oneexample, the analysis and calculation shown in FIG. 9 may, therefore,“exonerate” azithromycin from the risk of sudden death for all males andfor females outside the ages of 45-56.

In the additional embodiment illustrated in FIG. 10, the system andmethod of the present disclosure implement a plurality of modules forproviding real-time processing and delivery of calculated statisticsabout agents and outcomes. For example, the statistics may be presentedto a health care provider 110 via one or more health care providerapplications 756. In one implementation, health care organization 100includes a real-time transfer module 758. The real-time transfer module758 comprises computer executable instructions encoded on acomputer-readable medium, such as a hard drive, of one or more servercomputers controlled by the health care organization 100. Specifically,the real-time transfer module 758 is configured to calculate statistics,such a risk scores, for real-time information received via a network 760between the health care organization 100 and external systems andapplications. Preferably, the real-time transfer module 758 employs aservice-oriented architecture (SOA) by defining and implementing one ormore application platform-independent software services to carryreal-time data between various systems and applications.

In one embodiment, the real-time transfer module 758 comprises webservices 762, 764 that interface with external applications fortransporting the real-time data via a Simple Object Access Protocol(SOAP) over HTTP (Hypertext Transfer Protocol). The message ingest webservice 762, for example, receives real-time data that is subsequentlyprocessed in real-time by the calculation engine 126. The message ingestweb service 762 synchronously collects clinical data 114 from themedical insurance carrier 112, patient-entered data 128, includingpatient-entered clinical data 128, from the patient's PHR 108 and HRA130, as well as health reference information 122 and medical newsinformation 124. In an embodiment, the message ingest web service 762also receives clinical data 114 in real-time from one or more healthcare provider applications 756, such as an electronic medical record(EMR) application and a disease management application. In yet anotherembodiment, the message ingest service 762 receives at least some of thepatient-entered data 128 pursuant to the patient's interaction with anurse in disease management or an integrated voice response (IVR)system. Incoming real-time data is optionally stored in the medicaldatabase 118. Furthermore, incoming real-time data associated with agiven patient 102, in conjunction with previously stored data at thedatabase 118 and the clinical rules 120, defines a rules engine run tobe processed by the calculation engine 126. Hence, the real-timetransfer module 758 collects incoming real-time data from multiplesources and defines a plurality of rules engine runs associated with oneor more agents (e.g., drugs) and one or more outcomes (e.g., adverseevents) for real-time processing.

The real-time transfer module 758 forwards the rules engine runs to thecalculation engine 126 to instantiate a plurality of real-time ruleprocessing sessions 772. The processing of the rule processing sessions772 by the calculation engine 126 can be load-balanced across multiplelogical and physical servers to facilitate multiple and simultaneousrequests for real-time calculation of risk scores for one or more pairsof agents and outcomes. In one embodiment, the load-balancing ofsessions 772 is accomplished in accordance with a J2EE (Java)specification. Each rule processing session 772 makes calls to themedical database 118 by referring to a unique agent ID field for acorresponding agent (e.g., drug) to receive data related to that agentfor processing of incoming real-time data. The results 1000 of thereal-time processing of the calculation engine may then be output to thereal-time transfer module 758 for distribution to one or more healthcare provider applications 756 and/or to other servers and/or servicesvia message output service 764.

In sum, embodiments described herein provide a system and method forpharmacovigilance, i.e., drug surveillance. The systems and methodsdescribed herein may, in some implementations, be used by drug companiesor others (such as, for example, the FDA) to monitor and test the safetyand efficacy of drugs with respect to certain outcomes. The systems andmethods could be customized by applying certain filters to analyze thedata at finer granularity.

Some embodiments compute the clinical context of a health outcome oradverse event, rather than simply pairing a drug to a health outcome ofinterest. In various implementations, this includes analyzing theexistence of an FDA-labeled indication for the drug (i.e., on-label useversus off-label use), the relative frequency of the symptoms for theoutcome of interest (e.g., dizziness or palpitations may be symptoms ofan arrhythmia), the relative frequency of testing for the outcome ofinterest (e.g., Holter EKG monitoring may be used to detect arrhythmias)to calibrate whether frequency of the outcome of interest (e.g., theremay appear to be more liver abnormalities just because more liverfunction testing was being done), the relative frequency of the outcomeitself, and the relative frequency of “rescue treatments” related to theoutcome, e.g. for a drug that causes diarrhea, the frequency ofanti-diarrheal treatments (as opposed to episodes of the diarrheaitself).

Embodiments aggregate this data in a manner not only to detect newsignals of drug-adverse event relationships, but can be configured in away to “exonerate” or provide data to suggest that no agent-outcomerelationship was detected, even though the sample size suggests a highprobability that the relationship. In this way, drugs that may appear tobe generating signals in the FDA AERS (Adverse Event Reporting System)may be compared against the signal confirmation versus exonerationfindings calculated using embodiments of the disclosure. For example,using the embodiments disclosed herein, which are capable of updating ona near-real-time basis by running analysis on a frequent repeated basis(e.g., weekly, monthly), signals are detected earlier and trend analysisfor emerging and/or fading signals can be performed more quickly.

All references, including publications, patent applications and patents,cited herein are hereby incorporated by reference to the same extent asif each reference were individually and specifically indicated to beincorporated by reference and were set forth in its entirety herein.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) are to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Preferred embodiments of this disclosure are described herein, includingthe best mode known to the inventors for carrying out the disclosure.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the disclosure to be practicedotherwise than as specifically described herein. Accordingly, thisdisclosure includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the disclosure unlessotherwise indicated herein or otherwise clearly contradicted by context.

What is claimed is:
 1. A method for analyzing a relationship between anagent and one or more clinical outcomes, the method comprising:receiving a selection of a first agent; receiving a selection of one ormore clinical outcomes; analyzing, by a processor included in acomputing device, clinical data stored in a database to determine anumber of occurrences of each of the one or more clinical outcomes whenthe first agent is administered; calculating, by the processor, for thefirst agent, a first set of risk scores for each of the one or moreclinical outcomes, wherein calculating the risk score for a clinicaloutcome includes measuring a statistical significance of a relationshipbetween the first agent and the clinical outcome based on a total numberof patients in an entire population, a number of patients to whom thefirst agent is administered, a number of occurrences of the clinicaloutcome when the first agent is administered, and a total number ofpatients in the entire population that experienced the clinical outcome;outputting the first set of risk scores to a graphical display device,wherein, for the first agent, a first risk score indicates that thefirst agent is not the cause of a first clinical outcome for a firstsub-population that is a subset of the entire population; and generatinga first hypothesis by the processor that is based on the first riskscore that indicates that the first agent is not the cause of the firstclinical outcome for the first sub-population.
 2. The method of claim 1,further comprising: generating and outputting a message corresponding tothe first hypothesis generated by the processor.
 3. The method of claim1, further comprising: receiving a selection of a second agent;analyzing, by the processor, the clinical data to determine a number ofoccurrences of each of the one or more clinical outcomes when the secondagent is administered; calculating, by the processor, for the secondagent, a second set of risk scores for each of the one or more clinicaloutcomes; outputting the second set of risk scores to the graphicaldisplay device, wherein, for the second agent, a second risk scoreidentifies a possible benefit of the second agent for a secondsub-population that is a subset of the entire population; and generatinga second hypothesis by the processor that is based on the second riskscore that indicates that identifies the possible benefit of the secondagent for the second sub-population that is a subset of the entirepopulation.
 4. The method of claim 3, further comprising: generating andoutputting a message corresponding to the second hypothesis generated bythe processor.
 5. The method of claim 1, further comprising: filteringthe clinical data stored in the database based on one or more filters,such that the risk scores are calculated using data that satisfies theone or more filters, wherein the one or more filters include age,gender, clinical stratification scores and identified conditions, and/oran indication of use of the agent.
 6. The method of claim 1, furthercomprising calculating a confidence level for the first risk score basedon the equation:$\frac{{(I)\left\lbrack {{({IAO})\left( {I - {IO} - {IA} + {IAO}} \right)} - {\left( {{IO} - {IAO}} \right)\left( {{IA} - {IAO}} \right)}} \right\rbrack}^{2}}{({IA})\left( {I - {IA}} \right)({IO})\left( {I - {IO}} \right)}$wherein: I represents the total number of patients in the entirepopulation; IA represents the number of patients to whom the first agentis administered; IAO represents the number of occurrences of the firstclinical outcome when the first agent is administered; and IO representsthe total number of patients in the entire population that experiencedthe first clinical outcome.
 7. The method of claim 1, wherein the firstagent comprises a prescription drug.
 8. The method of claim 1, whereinthe first agent exhibits a relatively lower risk score for one of theclinical outcomes compared to the other agents in a common class ofagents.
 9. The method of claim 1, wherein the clinical data stored in adatabase includes demographic data, lab data, pharmacy data, claimsdata, diagnostic codes, procedure codes, heath reference information,medical news, standards-of-care, and/or patient-entered data.
 10. Themethod of claim 1, further comprising displaying, for each combinationof agent and clinical outcome, a circle corresponding to the risk score,wherein a larger circle corresponds to a larger risk score.
 11. Anon-transitory computer-readable storage medium storing instructionsthat when executed by a processor cause a computer system to analyze arelationship between an agent and one or more clinical outcomes, byperforming the steps of: receiving a selection of a first agent;receiving a selection of one or more clinical outcomes; analyzing, by aprocessor included in a computing device, clinical data stored in adatabase to determine a number of occurrences of each of the one or moreclinical outcomes when the first agent is administered; calculating, bythe processor, for the first agent, a first set of risk scores for eachof the one or more clinical outcomes, wherein calculating the risk scorefor a clinical outcome includes measuring a statistical significance ofa relationship between the first agent and the clinical outcome based ona total number of patients in an entire population, a number of patientsto whom the first agent is administered, a number of occurrences of theclinical outcome when the first agent is administered, and a totalnumber of patients in the entire population that experienced theclinical outcome; outputting the first set of risk scores to a graphicaldisplay device, wherein, for the first agent, a first risk scoreindicates that the first agent is not the cause of a first clinicaloutcome for a first sub-population that is a subset of the entirepopulation; and generating a first hypothesis by the processor that isbased on the first risk score that indicates that the first agent is notthe cause of the first clinical outcome for the first sub-population.12. The computer-readable storage medium of claim 11, furthercomprising: generating and outputting a message corresponding to thefirst hypothesis generated by the processor.
 13. The computer-readablestorage medium of claim 11, further comprising: receiving a selection ofa second agent; analyzing, by the processor, the clinical data todetermine a number of occurrences of each of the one or more clinicaloutcomes when the second agent is administered; calculating, by theprocessor, for the second agent, a second set of risk scores for each ofthe one or more clinical outcomes; outputting the second set of riskscores to the graphical display device, wherein, for the second agent, asecond risk score identifies a possible benefit of the second agent fora second sub-population that is a subset of the entire population; andgenerating a second hypothesis by the processor that is based on thesecond risk score that indicates that identifies the possible benefit ofthe second agent for the second sub-population that is a subset of theentire population.
 14. The computer-readable storage medium of claim 13,further comprising: generating and outputting a message corresponding tothe second hypothesis generated by the processor.
 15. Thecomputer-readable storage medium of claim 1, further comprising:filtering the clinical data stored in the database based on one or morefilters, such that the risk scores are calculated using data thatsatisfies the one or more filters, wherein the one or more filtersinclude age, gender, clinical stratification scores and identifiedconditions, and/or an indication of use of the agent.
 16. Thecomputer-readable storage medium of claim 11, further comprisingcalculating a confidence level for the first risk score based on theequation:$\frac{{(I)\left\lbrack {{({IAO})\left( {I - {IO} - {IA} + {IAO}} \right)} - {\left( {{IO} - {IAO}} \right)\left( {{IA} - {IAO}} \right)}} \right\rbrack}^{2}}{({IA})\left( {I - {IA}} \right)({IO})\left( {I - {IO}} \right)}$wherein: I represents the total number of patients in the entirepopulation; IA represents the number of patients to whom the first agentis administered; IAO represents the number of occurrences of the firstclinical outcome when the first agent is administered; and IO representsthe total number of patients in the entire population that experiencedthe first clinical outcome.
 17. The computer-readable storage medium ofclaim 11, wherein the first agent comprises a prescription drug.
 18. Thecomputer-readable storage medium of claim 11, wherein the first agentexhibits a relatively lower risk score for one of the clinical outcomescompared to the other agents in a common class of agents.
 19. Thecomputer-readable storage medium of claim 11, wherein the clinical datastored in a database includes demographic data, lab data, pharmacy data,claims data, diagnostic codes, procedure codes, heath referenceinformation, medical news, standards-of-care, and/or patient-entereddata.
 20. A system comprising: a clinical data database; and ahealthcare organization computing device executing one or moreprocessors to analyze a relationship between a prescription drug and oneor more clinical outcomes, by performing the steps of: receiving aselection of a first prescription drug; receiving a selection of one ormore clinical outcomes; analyzing, by a processor included in acomputing device, clinical data stored in a database to determine anumber of occurrences of each of the one or more clinical outcomes whenthe first prescription drug is administered; calculating, by theprocessor, for the first prescription drug, a first set of risk scoresfor each of the one or more clinical outcomes, wherein calculating therisk score for a clinical outcome includes measuring a statisticalsignificance of a relationship between the first prescription drug andthe clinical outcome based on a total number of patients in an entirepopulation, a number of patients to whom the first prescription drug isadministered, a number of occurrences of the clinical outcome when thefirst prescription drug is administered, and a total number of patientsin the entire population that experienced the clinical outcome;outputting the first set of risk scores to a graphical display device,wherein, for the first prescription drug, a first risk score indicatesthat the first prescription drug is not the cause of a first clinicaloutcome for a first sub-population that is a subset of the entirepopulation; and generating a first hypothesis by the processor that isbased on the first risk score that indicates that the first prescriptiondrug is not the cause of the first clinical outcome for the firstsub-population.